Fuzzy super resolution mapping based on markov random fields

V. A. Tolpekin; N. A.S. Hamm

doi:10.1109/IGARSS.2008.4779134

Fuzzy super resolution mapping based on markov random fields

V. A. Tolpekin, N. A.S. Hamm

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

Recent research has used Markov Random Fields (MRF) as a method for super-resolution mapping (SRM). This paper investigated the per-pixel uncertainty associated with MRF based SRM. This provided insight into the spatial distribution of uncertainty associated with. SRM. Furthermore, the map of per-pixel uncertainty clearly shows the boundary between land-cover classes and this may provide an input for image segmentation. The insight provided by the per-pixel. uncertainty together with the class boundaries will be valuable for development of the MRF approach to super-resolution mapping.

Original language	English
Title of host publication	2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
Pages	II875-II878
Edition	1
DOIs	https://doi.org/10.1109/IGARSS.2008.4779134
Publication status	Published - 2008
Externally published	Yes
Event	2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings - Boston, MA, United States Duration: 6 Jul 2008 → 11 Jul 2008

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Number	1
Volume	2

Conference

Conference	2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
Country/Territory	United States
City	Boston, MA
Period	6/07/08 → 11/07/08

Keywords

Fields
Image classification
Markov random
Superresolution mapping
Uncertainty

ASJC Scopus subject areas

Computer Science Applications
General Earth and Planetary Sciences

Access to Document

10.1109/IGARSS.2008.4779134

Cite this

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title = "Fuzzy super resolution mapping based on markov random fields",

abstract = "Recent research has used Markov Random Fields (MRF) as a method for super-resolution mapping (SRM). This paper investigated the per-pixel uncertainty associated with MRF based SRM. This provided insight into the spatial distribution of uncertainty associated with. SRM. Furthermore, the map of per-pixel uncertainty clearly shows the boundary between land-cover classes and this may provide an input for image segmentation. The insight provided by the per-pixel. uncertainty together with the class boundaries will be valuable for development of the MRF approach to super-resolution mapping.",

keywords = "Fields, Image classification, Markov random, Superresolution mapping, Uncertainty",

author = "Tolpekin, {V. A.} and Hamm, {N. A.S.}",

year = "2008",

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series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

number = "1",

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Tolpekin, VA & Hamm, NAS 2008, Fuzzy super resolution mapping based on markov random fields. in 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings. 1 edn, 4779134, International Geoscience and Remote Sensing Symposium (IGARSS), no. 1, vol. 2, pp. II875-II878, 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings, Boston, MA, United States, 6/07/08. https://doi.org/10.1109/IGARSS.2008.4779134

Fuzzy super resolution mapping based on markov random fields. / Tolpekin, V. A.; Hamm, N. A.S.
2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings. 1. ed. 2008. p. II875-II878 4779134 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2, No. 1).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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AU - Hamm, N. A.S.

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AB - Recent research has used Markov Random Fields (MRF) as a method for super-resolution mapping (SRM). This paper investigated the per-pixel uncertainty associated with MRF based SRM. This provided insight into the spatial distribution of uncertainty associated with. SRM. Furthermore, the map of per-pixel uncertainty clearly shows the boundary between land-cover classes and this may provide an input for image segmentation. The insight provided by the per-pixel. uncertainty together with the class boundaries will be valuable for development of the MRF approach to super-resolution mapping.

KW - Fields

KW - Image classification

KW - Markov random

KW - Superresolution mapping

KW - Uncertainty

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M3 - Conference contribution

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T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

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BT - 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings

T2 - 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings

Y2 - 6 July 2008 through 11 July 2008

ER -

Fuzzy super resolution mapping based on markov random fields

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this